High-Fidelity Meshes from Tissue Samples for Diffusion MRI Simulations

  • Eleftheria Panagiotaki
  • Matt G. Hall
  • Hui Zhang
  • Bernard Siow
  • Mark F. Lythgoe
  • Daniel C. Alexander
Conference paper

DOI: 10.1007/978-3-642-15745-5_50

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6362)
Cite this paper as:
Panagiotaki E., Hall M.G., Zhang H., Siow B., Lythgoe M.F., Alexander D.C. (2010) High-Fidelity Meshes from Tissue Samples for Diffusion MRI Simulations. In: Jiang T., Navab N., Pluim J.P.W., Viergever M.A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010. MICCAI 2010. Lecture Notes in Computer Science, vol 6362. Springer, Berlin, Heidelberg

Abstract

This paper presents a method for constructing detailed geometric models of tissue microstructure for synthesizing realistic diffusion MRI data. We construct three-dimensional mesh models from confocal microscopy image stacks using the marching cubes algorithm. Random-walk simulations within the resulting meshes provide synthetic diffusion MRI measurements. Experiments optimise simulation parameters and complexity of the meshes to achieve accuracy and reproducibility while minimizing computation time. Finally we assess the quality of the synthesized data from the mesh models by comparison with scanner data as well as synthetic data from simple geometric models and simplified meshes that vary only in two dimensions. The results support the extra complexity of the three-dimensional mesh compared to simpler models although sensitivity to the mesh resolution is quite robust.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Eleftheria Panagiotaki
    • 1
  • Matt G. Hall
    • 1
  • Hui Zhang
    • 1
  • Bernard Siow
    • 1
    • 2
  • Mark F. Lythgoe
    • 2
  • Daniel C. Alexander
    • 1
  1. 1.Centre for Medical Image Computing, Department of Computer ScienceUniversity College LondonUK
  2. 2.Centre for Advanced Biomedical ImagingUniversity College LondonUK

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